About: Aika is an open source text mining engine. It can automatically extract and annotate semantic information in text. In case this information is ambiguous, Aika will generate several hypothetical interpretations about the meaning of this text and retrieve the most likely one.

Aika Version 0.17 2018-05-14

• Introduction of synapse relations. Previously the relation between synapses was implicitly modeled through word positions (RIDs). Now it is possible to explicitly model relations like: The end position of input activation 1 equals to the begin position of input activation 2. Two types of relations are currently supported, range relations and instance relations. Range relations compare the input activation range of a given synapse with that of the linked synapse. Instance relations also compare the input activations of two synapses, but instead of the ranges the dependency relations of these activations are compared.
• Removed the norm term from the interpretation objective function.
• Introduction of an optional distance function to synapses. It allows to model a weakening signal depending on the distance of the activation ranges.
• Example implementation of a context free grammar.
• Example implementation for co-reference resolution.
• Work on an syllable identification experiment based on the meta network implementation.

Aika Version 0.15 2018-03-16

• Simplified interpretation handling by removing the InterpretationNode class and moving the remaining logic to the Activation class.
• Moved the activation linking and activation selection code to separate classes.
• Ongoing work on the training algorithms.

About: A WEKA package for analyzing emotion and sentiment of tweets.

Initial Announcement on mloss.org.

About: A thin Python3 wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls.

• upgraded to Weka 3.9.2
• properly initializing package support now, rather than adding package jars to classpath
• added weka.core.ClassHelper Java class for obtaining classes and static fields, as javabridge only uses the system class loader

About: A thin Python wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls.

• upgraded to Weka 3.9.2
• properly initializing package support now, rather than adding package jars to classpath
• added weka.core.ClassHelper Java class for obtaining classes and static fields, as javabridge only uses the system class loader

About: Kernel-based Learning Platform (KeLP) is Java framework that supports the implementation of kernel-based learning algorithms, as well as an agile definition of kernel functions over generic data representation, e.g. vectorial data or discrete structures. The framework has been designed to decouple kernel functions and learning algorithms, through the definition of specific interfaces. Once a new kernel function has been implemented, it can be automatically adopted in all the available kernel-machine algorithms. KeLP includes different Online and Batch Learning algorithms for Classification, Regression and Clustering, as well as several Kernel functions, ranging from vector-based to structural kernels. It allows to build complex kernel machine based systems, leveraging on JSON/XML interfaces to instantiate prediction models without writing a single line of code.

In addition to minor improvements and bug fixes, this release includes:

• The possibility to generate the Compositional GRCT and the Compositional LCT data structures in kelp-input-generator.

• New metrics for evaluating Classification Tasks.

• New Tutorial and Unit Tests.

Check out this new version from our repositories. API Javadoc is already available. Your suggestions will be very precious for us, so download and try KeLP 2.2.2!

About: The Weka workbench contains a collection of visualization tools and algorithms for data analysis and predictive modelling, together with graphical user interfaces for easy access to this [...]

This release include a lot of bug fixes and improvements. Some of these are detailed at

http://jira.pentaho.com/projects/DATAMINING/issues/DATAMINING-771

As usual, for a complete list of changes refer to the changelogs.

About: The Advanced Data mining And Machine learning System (ADAMS) is a flexible workflow engine aimed at quickly building and maintaining data-driven, reactive workflows, easily integrated into business processes.

Some highlights:

• Code base was moved to Github
• Nearly 90 new actors, 25 new conversions
• much improved deeplearning4j module
• experimental support for Microsoft's CNTK deep learning framework
• rsync module
• MEKA webservice module
• improved support for image annotations
• improved LaTeX support
• Websocket support

About: General purpose Java Machine Learning library for classification, regression, and clustering.

See github release tab for change info

About: FEAST provides implementations of common mutual information based filter feature selection algorithms (mim, mifs, mrmr, cmim, icap, jmi, disr, fcbf, etc), and an implementation of RELIEF. Written for C/C++ & Matlab.

Major refactoring of FEAST to improve speed and portability.

• FEAST now clones the input data if it's floating point and discretises it to unsigned ints once in a single pass. This improves the speed by about 30%.
• FEAST now has unsigned int entry points which avoid this discretisation and are much faster if the data is already categorical.
• Added weighted feature selection algorithms to FEAST which can be used for cost-sensitive feature selection.
• Added a Java API using JNI.
• FEAST now returns the internal score for each feature according to the criterion. Available in all three APIs.
• Rearranged the repository to make it easier to work with. Header files are now in `include`, source in `src`, the MATLAB API is in `matlab/` and the Java API is in `java/`.
• FEAST now compiles cleanly using `-std=c89 -Wall -Werror`.

About: DIANNE is a modular software framework for designing, training and evaluating artificial neural networks on heterogeneous, distributed infrastructure . It is built on top of OSGi and AIOLOS and can transparently deploy and redeploy (parts of) a neural network on multiple machines, as well as scale up training on a compute cluster.

Initial Announcement on mloss.org.

About: A Java Toolbox for Scalable Probabilistic Machine Learning.

• Added sparklink module implementing the integration with Apache Spark. More information here.
• Fluent pattern in latent-variable-models
• Predefined model implementing the concept drift detection

Detailed information can be found in the toolbox's web page

About: Automatically finds the best model with its best parameter settings for a given classification or regression task.

Initial Announcement on mloss.org.

About: Jatecs is an open source Java library focused on automatic text categorization.

Initial Announcement on mloss.org.

About: ELKI is a framework for implementing data-mining algorithms with support for index structures, that includes a wide variety of clustering and outlier detection methods.

Additions and improvements from ELKI 0.7.0 to 0.7.1:

Algorithm additions:

• GriDBSCAN: DBSCAN using grid partitioning (Minkowski distances only)

• Compare-Means and Sort-Means k-means variations (much faster than traditional k-means)

• Visualization of dendrograms.

Important bug fixes:

• Classes with no package ("default package") would cause errors.

• The fast power function implementation was sometimes returning incorrect results.

• Random sampling was sometimes not sampling from the full data set.

UI improvements:

• The file input source will now automatically choose the Arff parser for .arff files.

• MiniGUI now allows choosing other applications.

• MiniGUI now displays the command line in a separate field.

• MiniGUI displays an error message, if an incorrect classpath or JAyatana (on Ubuntu) is detected.

• Export to png now works, we added a work-around for an open Batik bug.

Smaller changes:

• Many smaller bug fixes.

• C-Index for cluster evaluation now can process larger data sets.

• OPTICS output of undefined reachability fixed.

• External distance matrixes are easier to use and perform additional checks.

• Precomputed distance matrixes can answer range and kNN queries.

• Voronoi visualization can be switched in the menu now.

• Improved backwards command line compatibility with additional aliases.

• Added generated @since annotations in JavaDoc.

• Many new unit tests, renamed to the Java conventions.

• Low-level reading of service files, to have faster startup.

About: Software for Automatic Construction and Inference of DBNs Based on Mathematical Models

Initial Announcement on mloss.org.

About: Easily prototype WEKA classifiers and filters using Python scripts.

0.3.0

• Filters have now been implemented.
• Classifier and filter classes satisfy base unit tests.

0.2.1

• Can now choose to save the script in the model using the -save flag.

0.2.0

• Added Python 3 support.
• Added uses decorator to prevent non-essential arguments from being passed.
• Fixed nasty bug where imputation, binarisation, and standardisation would not actually be applied to test instances.
• GUI in WEKA now displays the exception as well.
• Fixed bug where single quotes in attribute values could mess up args creation.
• ArffToPickle now recognises class index option and arguments.
• Fix nasty bug where filters were not being saved and were made from scratch from test data.

0.1.1

• ArffToArgs gets temporary folder in a platform-independent way, instead of assuming /tmp/.
• Can now save args in ArffToPickle using save.

0.1.0

• Initial release.

About: Apache Mahout is an Apache Software Foundation project with the goal of creating both a community of users and a scalable, Java-based framework consisting of many machine learning algorithm [...]

Apache Mahout introduces a new math environment we call Samsara, for its theme of universal renewal. It reflects a fundamental rethinking of how scalable machine learning algorithms are built and customized. Mahout-Samsara is here to help people create their own math while providing some off-the-shelf algorithm implementations. At its core are general linear algebra and statistical operations along with the data structures to support them. You can use is as a library or customize it in Scala with Mahout-specific extensions that look something like R. Mahout-Samsara comes with an interactive shell that runs distributed operations on a Spark cluster. This make prototyping or task submission much easier and allows users to customize algorithms with a whole new degree of freedom. Mahout Algorithms include many new implementations built for speed on Mahout-Samsara. They run on Spark 1.3+ and some on H2O, which means as much as a 10x speed increase. You’ll find robust matrix decomposition algorithms as well as a Naive Bayes classifier and collaborative filtering. The new spark-itemsimilarity enables the next generation of cooccurrence recommenders that can use entire user click streams and context in making recommendations.

About: The Cognitive Foundry is a modular Java software library of machine learning components and algorithms designed for research and applications.

• General:
  • Upgraded MTJ to 1.0.3.
• Common:
  • Added package for hash function computation including Eva, FNV-1a, MD5, Murmur2, Prime, SHA1, SHA2
  • Added callback-based forEach implementations to Vector and InfiniteVector, which can be faster for iterating through some vector types.
  • Optimized DenseVector by removing a layer of indirection.
  • Added method to compute set of percentiles in UnivariateStatisticsUtil and fixed issue with percentile interpolation.
  • Added utility class for enumerating combinations.
  • Adjusted ScalarMap implementation hierarchy.
  • Added method for copying a map to VectorFactory and moved createVectorCapacity up from SparseVectorFactory.
  • Added method for creating square identity matrix to MatrixFactory.
  • Added Random implementation that uses a cached set of values.
• Learning:
  • Implemented feature hashing.
  • Added factory for random forests.
  • Implemented uniform distribution over integer values.
  • Added Chi-squared similarity.
  • Added KL divergence.
  • Added general conditional probability distribution.
  • Added interfaces for Regression, UnivariateRegression, and MultivariateRegression.
  • Fixed null pointer exception that can happen in K-means with an empty cluster.
  • Fixed name of maxClusters property on AgglomerativeClusterer (was called maxMinDistance).
• Text:
  • Improvements to LDA Gibbs sampler.